Pith Number

pith:SVHAKK6R

pith:2026:SVHAKK6RETS42RVWU5KYGNI4AR

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TouchAnything: A Dataset and Framework for Bimanual Tactile Estimation from Egocentric Video

Chuqiao Lyu, Feiyang Hong, Guannan Zhang, Haotian Wu, Jianyi Zhou, Ruichen Zhen, Shuo Yang, Weisheng Dai, Wenbo Ding, Xushi Wang, Yinian Mao, Yuxiang Jiang, Zirui Liu, Ziteng Gao

Tactile pressure maps can be predicted from egocentric video of bimanual interactions by incorporating optional wrist views.

arxiv:2605.13083 v1 · 2026-05-13 · cs.RO

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4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Experiments show that incorporating wrist-mounted views generally improves tactile prediction over egocentric-only input, achieving up to 5.0% relative improvement in Contact IoU and 6.1% relative improvement in Volumetric IoU.

C2weakest assumption

That the wearable tactile sensors deliver accurate, dense, and synchronized ground-truth pressure maps suitable for supervising vision-based prediction models across diverse tasks and environments.

C3one line summary

EgoTouch is a new multi-view egocentric dataset with dense bimanual tactile supervision, and TouchAnything is a baseline framework showing that wrist views improve vision-based tactile prediction over egocentric input alone.

References

42 extracted · 42 resolved · 4 Pith anchors

[1] Dawadi, and Sushant Chalise 2026 · doi:10.1109/access.2025.3648171

[3] ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging 2019 · arXiv:1904.06830

[4] Narang, Karl Van Wyk, Umar Iqbal, Stan Birchfield, Jan Kautz, and Dieter Fox 2021

[5] Scaling egocentric vision: The epic-kitchens dataset 2018

[6] Actionsense: A multimodal dataset and recording framework for human activities using wearable sensors in a kitchen environment 2022

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:08:58.634525Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

954e052bd124e5cd46b6a75583351c044e9039a3abfd19d297ad3ccc0b6a4c92

Aliases

arxiv: 2605.13083 · arxiv_version: 2605.13083v1 · doi: 10.48550/arxiv.2605.13083 · pith_short_12: SVHAKK6RETS4 · pith_short_16: SVHAKK6RETS42RVW · pith_short_8: SVHAKK6R

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/SVHAKK6RETS42RVWU5KYGNI4AR \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 954e052bd124e5cd46b6a75583351c044e9039a3abfd19d297ad3ccc0b6a4c92

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "bfbba12fafe00240154fe6aa99d3cf8523b25a8b27f7f58213641e46e17ac75b",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-05-13T06:54:36Z",
    "title_canon_sha256": "b389464e5e7b1c730ec1812001d88ffe20fa01b3c46b2539cb4fa7abc368a355"
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  "source": {
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    "kind": "arxiv",
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